The copelabs/usense dataset (v. 2017-01-27)

Data concerning social interaction and propinquity based on wireless and bluetooth.

Contributed by S. Firdose, L. Lopes, W. Moreira, R. Sofia, P. Mendes.

This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: - SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName - DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName - Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) - PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING There are two tracesets. A first traceset has been collected relying on a first NSense version in 2015. Then, a second traceset has been collected in 2016, with a refined version of NSense. In all tracesets, devices have been carried around by people that share the same affiliation during their individual daily routines (24 hour periods).

This dataset comprises two tracesets collected in different years, for people carrying around Android smartphones with the open-source middleware installed.

The devices were carried around by people during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). Some people shared affiliation.

network configuration

The tracesets were collected opportunistically. No internet access was required.

data collection methodology

We set up experiments making use of Android smartphones (Android 4.2, Android 5.1). Each device had the open-source NSense middleware installed (https:\/\/github.com\/COPELABS-SITI\/NSense). The data was collected locally via NSense v1.0, and then aggregated.

For each traceset, we have carried several experiments. Then, on each experiment we have collected data for each device (1 folder per device) - sampled every minute. The source folder holds several .dat files:

Distance.dat has three columns: Timestamp, Destination Device Id, and Distance towards Destination Device Id. If -1: distance could not be computed.

Microphone.dat has two columns: Timestamp and Soundlevel(QUIET, NORMAL, ALERT and NOISY)

PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING

error

The relative distance is being computed via a propagation loss model (Wi-Fi), as NSense considers non-intrusive measurement. Android does not allow RSSI based measurement, as RSSI is hard-coded (constant, equal to 60). Due to this, there were several cases detected where the distance could not be computed. For those cases, the distance value is -1: from our measurement, we have detected distance between 0 and 100 meters. "-1" allows the researchers to detect that this is an abnormal behavior.

When writing a paper that uses CRAWDAD datasets, we would appreciate it if you could cite both the authors of the dataset and CRAWDAD itself, and identify the exact dataset using the appropriate version number. For this dataset, this citation would look like: